EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 40 (2021) ITM Web Conf., 40 (2021) 03046 References
Open Access
Issue
ITM Web Conf.
Volume 40, 2021
International Conference on Automation, Computing and Communication 2021 (ICACC-2021)
Article Number 03046
Number of page(s) 5
Section Computing
DOI https://doi.org/10.1051/itmconf/20214003046
Published online 09 August 2021
  1. S, Evaluation of Frequent Itemset Mining Platforms using Apriori and FP Growth algorithm, ICCM, vol. 2, pp. 1–6, (2019) [Google Scholar]
  2. Sanjay R, Arti K, Adaptive-Miner: an efficient distributed association rule mining algorithm on Spark, Springer, pp. 1-17, (2018) [Google Scholar]
  3. Singh S, Garg R, Performance optimization of MapReduce based apriori algorithm on Hadoop cluster, CEE, pp. 348–364, (2018) [Google Scholar]
  4. Diaby D, Fode C, S-FPG: A Parallel version of FP-growth algorithm under Apache Spark, IEEE, pp. 98–101, (2017) [Google Scholar]
  5. Xiujin S, Shazong C, DFPS: Distributed FP-Growth algorithm based on Spark, IEEE, pp. 1725–1731, (2017) [Google Scholar]
  6. Singh S, Garg R, Mishra P, Review of Apriori based algorithm on MapReduce framework, ICC, pp. 593–604, (2017) [Google Scholar]
  7. Zhaowei, Jianru Y, Association Rule based data imputations with Spark, CCIS, pp. 1725–1731, (2016) [Google Scholar]
  8. Xun Y, Zhang J Fidoop: Parallel mining of frequent itemsets using MapReduce, IEEE, pp. 313–325, (2016) [Google Scholar]
  9. Shaosong Y, Guoyan X, Zhijian W, The parallel Improved Apriori Algorithm research based on Spark, ICFCT, pp. 354–359, (2015) [Google Scholar]
  10. Ravi R, Performance Analysis of Apriori and FP Growth on different Mapreduce frameworks, DTU, pp. 1–7, (2015) [Google Scholar]
  11. Feng G, Yunlogo M, A Distributed frequent itemset mining algorithm based on Spark, IEEE, pp. 271–275, (2015) [Google Scholar]
  12. Sanjay R, Manohar K, R-Apriori: an efficient Apriori based algorithm on Spark, PIKM, pp. 1–8, (2015) [Google Scholar]
  13. Zhang F, Lin M, A distributed frequent itemset mining using Spark for big data analytics, CC, pp. 1493–1501, (2015) [Google Scholar]
  14. Singh S, Garg R, Performance analysis of Apriori algorithm with different data structure on Hadoop cluster, IJCA, pp. 45–51, (2015) [Google Scholar]
  15. Hongjian Q, Yihua H, YAFIM: A parallel frequent itemset mining algorithm with Spark, IEEE, pp. 1664–1671, (2014) [Google Scholar]
  16. Aavdh S, Ajeet K, Ashish M, An empirical analysis and comparison of Apriori and FP-Growth algorithm for frequent pattern mining, IEEE, pp. 1599–1602, (2014) [Google Scholar]
  17. Moens S, Aksehirli E, Frequent Itemset Mining for Big Data, IEEE, pp. 111–118, (2013) [Google Scholar]
  18. Frequent Itemset Mining Dataset Repository: http://fimi.ua.ac.be/data. Accessed 15 April 2021. [Google Scholar]
  19. Online Data Repository: https://www.kaggle.com/coldperformer/online-retail-data-v3. [Google Scholar]
  20. Online Data Repository: https://www.kaggle.com/rounakbanik/the-movies-dataset?select=ratings.csv. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.